Time-cost trade-off analysis in project management: An ant system approach (original) (raw)
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Using Ant Colony Optimization algorithm for solving project management problems
Expert Systems with Applications, 2009
Network analysis provides an effective practical system for planning and controlling large projects in construction and many other fields. Ant Colony System is a recent approach used for solving path minimization problems. This paper presents the use of Ant Colony Optimization (ACO) system for solving and calculating both deterministic and probabilistic CPM/PERT networks. The proposed method is investigated for a selected case study in construction management. The results demonstrate that -compared to conventional methods -ACO can produce good optimal and suboptimal solutions.
Mulit Objective Ant Colony Optimization for Time-Cost- Risk Trade for Consstruction Projects
Time, cost and risk of project delivery are among the crucial aspects of each project with both client and contractor striving to optimize the project duration and cost concurrently. Studies have been conducted to model the time–cost relationships, ranging from heuristic methods and mathematical approaches to genetic algorithms. Emergence of new contracts that place an increasing pressure on maximizing the quality of projects while minimizing its time and cost, require the development of innovative models considering risk in addition to time and cost. In this research, a meta-heuristic multi-colony ant algorithm is developed for optimization of three objectives time-cost-risk as a trade-off problem. An attempt is made to develop a Multi Objective Optimisation Model (MOOM) capable of minimizing time and cost of projects (inclusive of risk) as multi-objective optimization of time-cost-risk. The model this developed is then compared with similar model and the efficiency of this model is ascertained. An example is analyzed to illustrate the capabilities of the present method in generating optimal/near optimal solutions. The result thus obtained from the algorithm developed is compared with solutions of the problem obtained from MAWA (modified adaptive weight approach) approach adopted by Feng et al. 1997 and MOACO (multi objective ant colony optimization) adopted by Afshar et al. 2006 and it is found that the model developed gives efficient results when compared to MAWA and comparable results when compared to MOACO.
MULTI-OBJECTIVE OPTIMIZATION OF TIME-COST-RISK USING ANT COLONY OPTIMIZATION
Time, cost and risk of project delivery are among the crucial aspects of each project with both client and contractor striving to optimize the project duration and cost concurrently. Studies have been conducted to model the time-cost relationships, ranging from heuristic methods and mathematical approaches to genetic algorithms. Emergence of new contracts that place an increasing pressure on maximizing the quality of projects while minimizing its time and cost, require the development of innovative models considering risk in addition to time and cost. In this research, a meta-heuristic multi-colony ant algorithm is developed for optimization of three objectives time-cost-risk as a trade-off problem. An attempt is made to develop a Multi Objective Optimization Model (MOOM) capable of minimizing time and cost of projects (inclusive of risk) as multi-objective optimization of time-cost-risk. The model this developed is then compared with similar model and the efficiency of this model is ascertained. An example is analyzed to illustrate the capabilities of the present method in generating optimal/near optimal solutions. The result thus obtained from the algorithm developed is compared with solutions of the problem obtained from MAWA (modified adaptive weight approach) approach adopted by and MOACO (multi objective ant colony optimization) adopted by and it is found that the model developed gives efficient results when compared to MAWA and comparable results when compared to MOACO.
Project time-cost trade-off scheduling: a hybrid optimization approach
International Journal of Advanced Manufacturing Technology (ISI-indexed)
This paper develops a hybrid approach to stochastic time-cost trade off problem (STCTP) in PERT networks of project management, where activities are subjected to linear cost function. The main objective of proposed approach is to optimally improve the project completion probability in a prespecified deadline from a risky value to a confident predefined probability. For this purpose, we construct a non-linear mathematical program with decision variables of activity mean durations, in which the objective function is concerned with minimization of direct cost. In order to solve the constructed model, we present a hybrid approach based on cutting plane method and Monte Carlo (MC) simulation. In order to illustrate the process of proposed approach, the approach was coded using MATLAB 7.6.0 and two illustrative examples are discussed. The results obtained from the computational study show that the proposed algorithm is an effective approach for the STCTP.
In this paper, hybrid ant colony optimization (HAntCO) approach in solving multi-skill resource-constrained project scheduling problem (MS-RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with ant colony optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS-RCPSP. Experiments have been performed using artificially created dataset instances based on real-world ones. We published those instances that can be used as a benchmark. Presented results show that ACO-based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO.
International Journal of Advanced Manufacturing Technology (ISI-indexed)
This paper describes a new approach based on Ant Colony Optimization (ACO) metaheuristic and Monte Carlo (MC) simulation technique, for Project Crashing Problem (PCP) under uncertainties. To our knowledge, this is the first application of ACO technique for the Stochastic Project Crashing Problem (SPCP), in the published literature. A Confidence Level based approach has been proposed for SPCP in PERT type networks, where activities are subjected to discrete cost functions and assumed to be exponentially distributed. The objective of proposed model is to optimally improve the project completion probability in a prespecified due date based on a predefined probability. In order to solve the constructed model, we apply the ACO algorithm and Path Criticality Index (PCI), together. The proposed approach applies the path criticality concept in order to select Most Critical Path (MCP) by using Monte Carlo simulation technique. Then the developed ACO is used to solve a nonlinear integer mathematical programming for selected path. In order to demonstrate the model effectiveness, a large scale illustrative example has been presented and several computational experiments are conducted to determine the appropriate levels of ACO parameters which lead to the accurate results with reasonable computational time. Finally, a comparative study has been conducted to validate the ACO approach, using several randomly generated problems.
In our country, we are still facing issues such as time and cost overruns which has led to large delays in construction projects in turn has effected the common people. According to the Ministry of Statistics and Programme Implementation as many as nearly 245 Central Sector Infrastructure Projects out of the 1315 projects have shown time overrun and nearly 350 of them have shown cost escalation. There are cases where the projects have shown both time and cost overrun and the number goes up to 98 to be exact. Managing the time, Cost and quality in a construction project has become a tough challenge for a project manager in our country. In our project, we are representing a clear difference between Microsoft Project and Ant Colony optimization which makes it cost effective and in turn helps to reduce the duration of work to be done. Nearly 85% of construction companies still use the traditional MS Excel to schedule their activities and duration and among all of them many felt the need to adopt Modern Software.
Using an Enhanced Ant Colony System to Solve Resource- Constrained Project Scheduling Problem
This study presents and evaluates a modified ant colony optimization (ACO) approach for the resource-constrained project scheduling problems. A modified ant colony system is proposed to solve the resource-constrained scheduling problems. A two-dimensional matrix is proposed in this study for scheduling activities with time, and it has a parallel scheme for solving project scheduling problems. There are two designed heuristic is proposed. The dynamic rule is designed to modify the latest starting time of activities and hence the heuristic function. In exploration of the search solution space, this investigation proposes a delay solution generation rule to escape the local optimal solution. Simulation results demonstrate that the proposed modified ant colony system algorithm provides an effective and efficient approach for solving project scheduling problems with resource constraints.
2009
, 227 pages In this thesis, meta-heuristic algorithms are developed to obtain optimum or near optimum solutions for the time-cost-resource trade-off and resource leveling problems in project scheduling. Time cost trade-off, resource leveling, single-mode resource constrained project scheduling, multi-mode resource constrained project scheduling and resource constrained time cost trade-off problems are analyzed. Genetic algorithm simulated annealing, quantum simulated annealing, memetic algorithm, variable neighborhood search, particle swarm optimization, ant colony optimization and electromagnetic scatter search meta-heuristic algorithms are implemented for time cost trade-off problems with unlimited resources. In this thesis, three new meta-heuristic algorithms are developed by embedding meta-heuristic algorithms in each other. Hybrid genetic algorithm with simulated annealing presents the best results for time cost trade-off. Resource leveling problem is analyzed by five genetic algorithm based metaheuristic algorithms. Apart from simple genetic algorithm, four meta-heuristic algorithms obtained same schedules obtained in the literature. In addition to this, in one of the test problems the solution is improved by the four meta-heuristic algorithms. v For the resource constrained scheduling problems; genetic algorithm, genetic algorithm with simulated annealing, hybrid genetic algorithm with simulated annealing and particle swarm optimization meta-heuristic algorithms are implemented. The algorithms are tested by using the project sets of Kolisch and Sprecher (1996). Genetic algorithm with simulated annealing and hybrid genetic algorithm simulated annealing algorithm obtained very successful results when compared with the previous state of the art algorithms. 120-activity multi-mode problem set is produced by using the single mode problem set of Kolisch and Sprecher (1996) for the analysis of resource constrained time cost trade-off problem. Genetic algorithm with simulated annealing presented the least total project cost.